Error estimate of discriminant for normal distribution.
E = TESTN(W,U,G,N)
| W|| Trained classifier mapping|
| U|| C x K dataset with C class means, labels and priors (default: [0 .. 0]) |
| G|| K x K x C matrix with C class covariance matrices (default: identity)|
| N|| Number of test examples (default 10000) |
This routine estimates as good as possible the classification error of Gaussian distributed problems with known means and covariances. N normally distributed data vectors with means, labels and prior probabilities defined by the dataset U (size [C,K]) and covariance matrices G (size [K,K,C]) are generated with the specified labels and are tested against the discriminant W. The fraction of incorrectly classified data vectors is returned. If W is a linear 2-class discriminant and N is not specified, the error is computed analytically.
mappings, datasets, qdc, nbayesc, testc,
|This file has been automatically generated. If badly readable, use the help-command in Matlab.|